A comprehensive security framework for cloud-based remote sensing image storage and retrieval with adversarial attack resistance

Remote sensing and satellite imaging have become essential in various geological and surveillance applications. These systems often rely on cloud platforms for storing satellite and aerial images, introducing trust and security concerns, especially in sensitive domains like border surveillance, monitoring, and reconnaissance. Traditional cloud solutions are prone to data breaches and adversarial attacks, highlighting the need for a secure, end-to-end framework. To address this, we propose a comprehensive security architecture for storing and retrieving sensitive remote sensing images. Our system ensures confidentiality, integrity, and access control, while also resisting adversarial attacks during image retrieval. It adopts a three-phase structure: secure authentication, secure storage, and secure retrieval. Authentication is achieved using a combination of Zero-Knowledge Proof and Quantum Key Distribution, establishing a tamper-proof user verification process. In the storage phase, quantum-based cryptography secures the images, while a deep hashing model resistant to adversarial attacks enables efficient indexing and retrieval. A watermark embedding mechanism helps detect insider threats and support forensic tracking in case of data breaches. Evaluated on a remote sensing image dataset using various backbone networks, our system achieved a retrieval accuracy of 94.77%, outperforming existing models by 8–12%. The framework is well-suited for high-security environments, including military applications.

遥感与卫星成像技术已在各类地质与监测应用中成为不可或缺的核心手段。此类系统通常依托云平台存储卫星与航空影像，由此引发了信任与安全层面的隐患，在边境监视、态势监测与侦察等敏感领域中这一问题尤为突出。传统云解决方案极易遭受数据泄露与对抗性攻击，这凸显了构建安全端到端框架的迫切需求。为此，我们提出了一种用于敏感遥感影像存储与检索的综合安全架构。本系统可保障影像的机密性、完整性与访问控制能力，同时能够抵御影像检索过程中的对抗性攻击。系统采用三阶段架构：安全认证、安全存储与安全检索。认证环节结合零知识证明（Zero-Knowledge Proof）与量子密钥分发（Quantum Key Distribution）技术，构建了防篡改的用户验证流程。存储阶段中，基于量子的加密技术可为影像提供安全防护，同时采用抗对抗性攻击的深度哈希模型实现高效的索引构建与影像检索。嵌入水印的机制则可协助检测内部威胁，并在发生数据泄露时支持溯源取证。在采用多种骨干网络的遥感影像数据集上进行的评估结果显示，本系统的检索准确率达到94.77%，较现有模型提升了8%至12%。该框架非常适用于包括军事应用在内的高安全等级场景。